US10345386B2 - Battery state estimation device and method of estimating battery state - Google Patents

Battery state estimation device and method of estimating battery state Download PDF

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US10345386B2
US10345386B2 US15/118,426 US201515118426A US10345386B2 US 10345386 B2 US10345386 B2 US 10345386B2 US 201515118426 A US201515118426 A US 201515118426A US 10345386 B2 US10345386 B2 US 10345386B2
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soc
battery
terminal voltage
estimated
equivalent circuit
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US20170176540A1 (en
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Toru Omi
Kenichi Miura
Satoru Hiwa
Takuma Iida
Kazushige Kakutani
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Panasonic Automotive Systems Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/367—Software therefor, e.g. for battery testing using modelling or look-up tables
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/382—Arrangements for monitoring battery or accumulator variables, e.g. SoC
    • G01R31/3842—Arrangements for monitoring battery or accumulator variables, e.g. SoC combining voltage and current measurements
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
    • G01R31/36—Arrangements for testing, measuring or monitoring the electrical condition of accumulators or electric batteries, e.g. capacity or state of charge [SoC]
    • G01R31/389—Measuring internal impedance, internal conductance or related variables
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/06—Lead-acid accumulators
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/4285—Testing apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/48—Accumulators combined with arrangements for measuring, testing or indicating the condition of cells, e.g. the level or density of the electrolyte
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M10/00—Secondary cells; Manufacture thereof
    • H01M10/42—Methods or arrangements for servicing or maintenance of secondary cells or secondary half-cells
    • H01M10/44—Methods for charging or discharging
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2220/00—Batteries for particular applications
    • H01M2220/20—Batteries in motive systems, e.g. vehicle, ship, plane
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02E60/10—Energy storage using batteries
    • Y02E60/126—
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries
    • Y02T10/7016—

Definitions

  • the present invention relates to a battery state estimation device for estimating an internal state of a battery with high accuracy, and to a method of estimating a battery state.
  • a vehicle powered primarily by an engine includes a battery serving as a power source for a starter motor used to start the engine.
  • a typical example of such a battery is a lead-acid battery.
  • charge-discharge characteristics of a lead-acid battery have improved.
  • a lead-acid battery is increasingly common as a power source for a special electric vehicle, such as an electric cart and a fork lift, which conventionally uses a lithium-ion secondary battery so expensive as to make the special electric vehicle unprofitable.
  • a dead battery or a battery that has been degraded in performance ranks first in a number of troubles that private vehicles suffer (specifically, the number indicates how many times Japan Automobile Federation (JAF) is called to come to the rescue of vehicles).
  • JAF Japan Automobile Federation
  • a stop-start system has become more common in a vehicle powered primarily by an engine in an effort to reduce emissions.
  • a remaining capacity of a battery may decrease to a point where the battery cannot generate an output high enough to restart the engine. Accordingly, it is desirable to detect a remaining capacity of a battery with high accuracy so that such a battery problem is prevented (see, for example, PTL 1).
  • an open circuit voltage hereinafter referred to as “OCV”
  • a remaining capacity of a lead-acid battery are known to be linearly related.
  • PTL 1 describes a technique of calculating a remaining capacity, based on an OCV measured, using the linear relationship.
  • PTL 2 discloses an invention that accurately estimates a state of charge (hereinafter referred to as “SOC”), which is a remaining capacity of a battery, by constructing an equivalent circuit model of the battery in consideration of a polarization component and estimating an internal state of the battery with high accuracy.
  • SOC state of charge
  • Exemplary embodiments of the present invention provide a battery state estimation device and a method of estimating a battery state which increase accuracy in both a terminal voltage estimation and an SOC estimation associated with the terminal voltage estimation by using a simple construction.
  • the battery state estimation device includes a detecting part, an SOC estimating part, an OCV estimating part, a terminal voltage estimating part, and a correcting part.
  • the detecting part detects a charge-discharge current and a terminal voltage of a battery.
  • the SOC estimating part estimates an SOC of the battery, based on the charge-discharge current detected by the detecting part.
  • a method of estimating a battery state includes the steps of: detecting a charge-discharge current and a terminal voltage of a battery; estimating an SOC of the battery, based on the charge-discharge current detected in the detecting step; estimating an OCV of the battery, based on the SOC estimated in the SOC estimating step and a relationship between an OCV and the SOC of the battery; calculating an estimated terminal voltage, based on the charge-discharge current and the terminal voltage detected in the detecting step and an equivalent circuit model constructed using an inversely proportional curve (i.e., an equivalent circuit model constructed using a function that is inversely proportional to a power); and correcting the SOC estimated in the SOC estimating step, based on the estimated terminal voltage calculated in the terminal voltage estimating step and the terminal voltage detected in the detecting step.
  • an equivalent circuit model constructed using an inversely proportional curve
  • the exemplary embodiments of the present invention enable a state estimation that considers a slow-response component of a battery without using a higher-order equivalent circuit model. Consequently, accuracy in both a terminal voltage estimation and an associated SOC estimation for a battery improves by using a simple construction.
  • FIG. 1A is a block diagram illustrating a configuration of a battery state estimation device according to a first exemplary embodiment of the present invention.
  • FIG. 2 illustrates a terminal voltage estimation model, which is a first-order equivalent circuit.
  • FIG. 3 illustrates an error between measured terminal voltages and terminal voltages estimated using the terminal voltage estimation model, which is the first-order equivalent circuit.
  • FIG. 4 is an enlarged view of a portion of FIG. 3 within a dotted line.
  • FIG. 5 illustrates a terminal voltage estimation model constructed using an inversely proportional curve.
  • FIG. 6 illustrates a comparison between terminal voltages estimated using the terminal voltage estimation model, which is the first-order equivalent circuit, and terminal voltages estimated using the terminal voltage estimation model constructed using the inversely proportional curve.
  • FIG. 7 illustrates an example of a function of the inversely proportional curve.
  • FIG. 8 illustrates examples of a function that is inversely proportional to a power.
  • FIG. 11 illustrates an error between measured terminal voltages and terminal voltages estimated with the terminal voltage estimation model constructed using the inversely proportional curve.
  • FIG. 12 is an enlarged view of a portion of FIG. 11 within a dotted line.
  • FIG. 14 illustrates a processing algorithm of a Kalman filter SOC estimating part illustrated in FIG. 13 .
  • a terminal voltage is estimated by identifying parameters of a battery model in an SOC estimation using a Kalman filter, as described in the PTL 2.
  • a first-order equivalent circuit model constructed using an exponential function cannot express a slow-response component (i.e., a polarization relaxation component) when estimating a terminal voltage.
  • Expressing the slow-response component requires a higher-order equivalent circuit model, which greatly increases computational effort and processing time for an ECU (Electrical Control Unit) having a limited processing capacity, making the estimation impractical.
  • FIG. 1A is a block diagram illustrating a configuration of a battery state estimation device according to a first exemplary embodiment of the present invention.
  • the battery state estimation device is, for example, an ECU.
  • the battery state estimation device includes sensor 100 , ARX (Autoregressive Exogenous) model identifying part 101 , equivalent circuit parameter estimating part 102 , OCV-SOC map storing part 103 , Kalman filter SOC estimating part 104 , and error calculating part 105 .
  • Sensor 100 measures a charge-discharge current and a terminal voltage of a battery (e.g., a secondary rechargeable battery such as a lead-acid battery used for activating a stop-start system).
  • Sensor 100 includes, for example, a current sensor and a voltage sensor.
  • Charge-discharge current i L and terminal voltage v T measured by sensor 100 are output, as appropriate, to ARX model identifying part 101 , equivalent circuit parameter estimating part 102 , OCV-SOC map storing part 103 , Kalman filter SOC estimating part 104 , and error calculating part 105 .
  • Charge-discharge current i L and terminal voltage v T are used in computations performed by the respective parts.
  • ARX model identifying part 101 , equivalent circuit parameter estimating part 102 , OCV-SOC map storing part 103 , Kalman filter SOC estimating part 104 , and error calculating part 105 are each constituted by hardware including a central processing unit (CPU), a memory, and a random access memory (RAM), which are all not shown.
  • the hardware components may be consolidated into an integrated circuit (e.g., a large scale integration (LSI)).
  • ARX model identifying part 101 , equivalent circuit parameter estimating part 102 , OCV-SOC map storing part 103 , Kalman filter SOC estimating part 104 , and error calculating part 105 each include, as software, programs. The computations are processed by the CPU, based on pre-stored data and programs stored on the memory (not shown). Results of the computations are temporarily stored on the RAM (not shown) for subsequent processes.
  • FIG. 2 illustrates a terminal voltage estimation model, which is a first-order equivalent circuit.
  • a state space representation of the model is given by the following formula:
  • QR is a nominal capacity of a battery.
  • Equivalent circuit parameters of the first-order equivalent circuit model are estimated by comparing a transfer function calculated by ARX model identifying part 101 with a transfer function calculated by equivalent circuit parameter estimating part 102 .
  • regression coefficients a 1 , . . . , a p , b 0 , . . . , b q are determined so that y(k) ⁇ (k) ⁇ is minimum.
  • y ( k ) [ ⁇ y ( k ⁇ 1) . . . ⁇ y ( k ⁇ p ) u ( k ) . . . u ( k ⁇ q )][ a 1 . . . a p b 0 . . . b q ] T +e ( k ) ⁇ ( k ) ⁇ + e ( k ) [Formula 4]
  • a transfer function of the ARX model is given by the following formula:
  • ARX model identifying part 101 performs a digital z-transformation on an amount of change in charge-discharge current i L and terminal voltage v T output from sensor 100 so that the following formula is obtained:
  • Equivalent circuit parameter identifying part 102 estimates parameters of an equivalent circuit by comparing the transfer function of the ARX model with a transfer function of an equivalent circuit. This relationship is given by the following formula:
  • Equivalent circuit parameter estimating part 102 estimates parameters R 0 , R 1 , C 1 of the equivalent circuit model illustrated in FIG. 2 , based on the formula, and outputs the estimated parameters to Kalman filter SOC estimating part 104 .
  • ARX model identifying part 101 and equivalent circuit parameter estimating part 102 perform the processes with a predetermined sampling period of, for example, 0.05 [s] to update the parameters.
  • OCV-SOC map storing part 103 outputs, to Kalman filter SOC estimating part 104 , information on an OCV-SOC map that OCV-SOC map storing part 103 pre-stores and which indicates a relationship between an OCV and an SOC.
  • the OCV-SOC relationship is indicated by a linear function.
  • OCV-SOC map storing part 103 outputs the preset values to ARX model identifying part 101 and equivalent circuit parameter estimating part 102 .
  • at least the regression coefficient needs to be output as the OCV-SOC map from OCV-SOC map storing part 103 .
  • a map may be selected, or a plurality of maps may be selected based on a type of a battery.
  • OCV-SOC map storing part 103 may determine a type of a battery, based on measured charge-discharge current value i L and measured terminal voltage value v T output from sensor 100 , and select an OCV-SOC map corresponding to the type of the battery. This configuration prevents or inhibits decrease in accuracy in an SOC estimation even if the battery is replaced.
  • Kalman filter SOC estimating part 104 estimates a terminal voltage and an SOC by using the following state-space representation:
  • Kalman filter SOC estimating part 104 performs the estimations by using the following formulae, as will be described in detail later.
  • current integration SOC estimation processing 200 As a processing algorithm, current integration SOC estimation processing 200 , estimated value correction processing 201 , OCV estimation processing 202 , inversely proportional curve-applied model processing 203 , and Kalman gain processing 204 are performed.
  • Kalman filter SOC estimating part 104 estimates an SOC by integrating charge-discharge current i L output from sensor 100 .
  • estimated value correction processing 201 Kalman filter SOC estimating part 104 corrects an estimated SOC′ by using a Kalman gain described later. The resultant estimated SOC is output as a present SOC to an external element.
  • Kalman filter SOC estimating part 104 calculates an estimated OCV, based on an OCV-SOC map output from OCV-SOC map storing part 103 and on the resultant estimated SOC.
  • Kalman filter SOC estimating part 104 calculates an estimated terminal voltage, based on equivalent circuit parameters output from equivalent circuit parameter estimating part 102 , charge-discharge current value i L output from sensor 100 , and an estimated OCV output in OCV estimation processing 202 .
  • Error calculating part 105 calculates error e[k] between the estimated terminal voltage and terminal voltage v T output from sensor 100 .
  • Kalman gain processing 204 Kalman filter SOC estimating part 104 corrects error e[k] by multiplying error e[k] by Kalman gain g[k].
  • FIG. 3 illustrates an error between measured terminal voltage v T and terminal voltage v T estimated using the terminal voltage estimation model (the first-order equivalent circuit) illustrated in FIG. 2 .
  • FIG. 4 is an enlarged view of a portion of FIG. 3 within a dotted line. As shown in FIGS. 3 and 4 , the error between the estimated values and the measured values increases over time because a first-order equivalent circuit constructed using an exponential function (Exp function) cannot express a polarization component of a long time constant.
  • Exp function exponential function
  • Kalman filter SOC estimating part 104 in performing inversely proportional curve-applied model processing 203 , estimates a terminal voltage and an SOC, using a terminal voltage estimation model constructed using an inversely proportional curve.
  • FIG. 5 illustrates the terminal voltage estimation model constructed using the inversely proportional curve.
  • a first-order resistor-capacitor parallel circuit (illustrated in FIG. 2 ) is replaced with inversely proportional curve-applied part 500 .
  • This terminal voltage estimation model constructed using the inversely proportional curve can express a slow-response component of a battery without using a higher-order resistor-capacitor parallel circuit.
  • FIG. 6 illustrates a comparison between terminal voltages estimated with the terminal voltage estimation model, which is the first-order equivalent circuit, and terminal voltages estimated with the terminal voltage estimation model constructed using the inversely proportional curve.
  • an Exp curve obtained with the terminal voltage estimation model, which is the first-order equivalent circuit converges more rapidly over time than does an inversely proportional curve obtained with the terminal voltage estimation model constructed using the inversely proportional curve.
  • This characteristic makes it difficult for the Exp curve to express a slow-response component of a battery.
  • the inversely proportional curve converges more slowly than does the Exp curve. This characteristic enables the inversely proportional curve to be variable when the Exp curve has converged and thus to express a slow-response component of a battery (as shown in a dotted box).
  • a present state estimation value and a state estimation value one step later are obtained using a Kalman filter from a present input value, a measured value, and a state estimation value one step before.
  • an extended Kalman filter is used for a terminal voltage estimation model constructed using a non-linear inversely proportional curve.
  • FIG. 8 illustrates an example of a function that is inversely proportional to a power.
  • multiplier p appropriate to a function inversely proportional to a power may be set, based on at least one of conditions including an estimated SOC, an estimated terminal voltage, and a type of a battery.
  • Kalman filter SOC estimating part 104 may set larger multiplier p for a battery in which an amount of change in a slow-response component is large.
  • x[k] ⁇ x[k] may be replaced with x[k] ⁇ x[k+1]. If present value ⁇ target value in that case, a computation is performed as follows:
  • ⁇ y - K x ⁇ v - target value
  • the formulae are used to form the following formula for expressing an estimated terminal voltage.
  • Kalman filter SOC estimating part 104 includes constant proportionality k in a state vector, and performs a simultaneous optimization using an extended Kalman filter.
  • Kalman filter SOC estimating part 104 corrects R 1 ′ so that R 1 ′ equals a value that is obtained by multiplying, by a constant, resistance value R 1 of a first-order equivalent circuit which is estimated by equivalent circuit parameter estimating part 102 .
  • the factor of correction may be changed in charging and discharging because an attenuation characteristic of a terminal voltage is different in both the operations.
  • a terminal voltage is estimated using the equivalent circuit model constructed using the inversely proportional curve.
  • This configuration enables a state estimation that considers a slow-response component of a battery, without using a higher-order equivalent circuit model.
  • the first-order equivalent circuit model of simple construction enables a terminal voltage estimation and an associated SOC estimation for a battery.
  • the present exemplary embodiment is applicable to an ECU that is designed for a stop-start system and which is limited in its processing ability because the present exemplary embodiment keeps an operational load low while preventing or inhibiting decrease in accuracy of the estimations.
  • a battery state estimation device illustrated in FIG. 13
  • configurations and elements are identical to those of the first exemplary embodiment, except that remaining battery capacity estimating part 106 and OCV-SOC map estimating part 110 perform their processes, and Kalman filter SOC estimating part 104 performs estimation in a different way.
  • a formula for calculating an OCV (Formula 22) and a formula for estimating a terminal voltage (Formula 24) in the present exemplary embodiment are different from those of the first exemplary embodiment in that the formulae (Formula 22 and Formula 24) in the first exemplary embodiment include b0 in a state vector whereas the formulae (Formula 22 and Formula 24) in the present exemplary embodiment include b0 in an input vector.
  • the OCV calculation in the present exemplary embodiment is given by the following formula:
  • Kalman filter SOC estimating part 104 calculates an estimated polarization voltage, based on an error between an estimated OCV estimated in OCV estimation process 202 and an estimated terminal voltage estimated in inversely proportional curve-applied model processing 203 , and outputs the estimated polarization voltage.
  • Remaining battery capacity estimating part 106 illustrated in FIG. 16 estimates a remaining battery capacity so that an estimated SOC estimated by Kalman filter SOC estimating part 104 equals an estimated SOC obtained using a current integration method.
  • An equation of state is given by:
  • SOC [l] corresponds to an estimated SOC that is output from Kalman filter SOC estimating part 104 when remaining battery capacity estimating part 106 starts estimation.
  • the estimation by remaining battery capacity estimating part 106 is timed to start in this way because there are time delays between the estimation by remaining battery capacity estimating part 106 and the estimation by Kalman filter SOC estimating part 104 .
  • a process (illustrated in FIG. 16 ) performed by remaining battery capacity estimating part 106 will be described.
  • an estimated remaining capacity of a battery is calculated in remaining capacity estimation processing 400 .
  • current integration SOC estimation processing 401 an estimated SOC is estimated using a current integration method from the estimated remaining capacity of the battery and charge-discharge current value i L of the battery output from sensor 100 .
  • error calculation processing 403 an error is calculated between the estimated SOC estimated in current integration SOC estimation processing 401 and an estimated SOC estimated by Kalman filter SOC estimating part 104 .
  • Kalman gain processing 404 a correction rate is calculated for the estimated SOC, based on the calculated error.
  • estimated value correction processing 402 the remaining capacity estimated in remaining capacity estimation processing 400 is corrected at the correction rate calculated in Kalman gain processing 404 .
  • remaining battery capacity estimating part 106 operates to perform the estimation processing over a longer period than does Kalman filter SOC estimating part 104 .
  • a time constant of a remaining battery capacity is longer than that of an SOC. Accordingly, if remaining battery capacity estimating part 106 operates to perform the estimation processing over a period identical to that of Kalman filter SOC estimating part 104 , results of the estimation by remaining battery capacity estimating part 106 vary greatly, reducing accuracy in the estimation. The prediction accuracy is prevented from decreasing by configuring remaining battery capacity estimation part 106 to operate to perform the estimation processing over a longer period than that of Kalman filter SOC estimating part 104 .
  • OCV-SOC map estimating part 110 illustrated in FIG. 15 reads an OCV-SOC map from OCV-SOC map storing part 103 and corrects the OCV-SOC map, rather than simply reading the OCV-SOC map.
  • OCV-SOC map estimating part 110 estimates a relationship between an OCV and an SOC of a battery, based on an estimated polarization voltage and an estimated SOC estimated by Kalman filter SOC estimating part 104 and on charge-discharge current value i L and terminal voltage value v T which sensor 100 detects and outputs.
  • the regression coefficient, [b 0 , b 1 ] T is used in a linear approximation for ease of calculation, but a determinant [b 0 , b 1 , b 2 , . . . b N ] T is used in the case of a polynomial of degree n.
  • terminal voltage estimation processing 301 a terminal voltage is estimated from an estimated polarization voltage and an estimated SOC output from Kalman filter SOC estimating part 104 and on random numbers generated in OCV-SOC map regression coefficient estimation processing 300 .
  • error calculation processing 303 an error is calculated between estimated terminal voltage v T [k] estimated in terminal voltage estimation processing 301 and measured terminal voltage v T output from sensor 100 .
  • Kalman gain processing 304 Kalman gain is calculated, based on the error calculated in error calculation processing 303 .
  • the regression coefficient, [b 0 , b 1 ] T serving as a state variable is corrected using, as a correction rate, the Kalman gain calculated in Kalman gain processing 304 .
  • the corrected regression coefficient is output to Kalman filter SOC estimating part 104 .
  • OCV-SOC map estimating part 110 may operate to perform the estimation processing over a longer period than does Kalman filter SOC estimating part 104 , as with the period with which remaining battery capacity estimation part 106 operates to perform the estimation processing. This configuration prevents accuracy of the estimation from decreasing, as in the case of SOC estimation part 106 .
  • the equivalent circuit parameters are estimated using ARX model identifying part 101 and equivalent circuit parameter estimating part 102 , but the equivalent circuit parameters may be estimated by Kalman filter SOC estimating part 104 .
  • ARX model identifying part 101 and equivalent circuit parameter estimating part 102 may be eliminated by including the equivalent circuit parameter in an equation of state used by Kalman filter SOC estimating part 104 .
  • ARX model identifying part 101 may be eliminated also by using other models other than ARX models, and other methods.
  • the battery state estimation device and the method of estimating a battery state according to the exemplary embodiments of the present invention are useful for detecting a state of a lead-acid battery for use in starting, especially, a vehicle designed with a stop-start system.

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US11221370B2 (en) * 2017-06-29 2022-01-11 Kabushiki Kaisha Toshiba Remaining battery energy estimation device, remaining battery energy estimation method, and storage medium

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